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| model: | |
| target: Models.interpretable_diffusion.gaussian_diffusion.Diffusion_TS | |
| params: | |
| seq_length: 240 | |
| feature_size: 3 | |
| n_layer_enc: 8 | |
| n_layer_dec: 5 | |
| d_model: 128 # 4 X 16 | |
| timesteps: 1000 # diffusion timesteps | |
| sampling_timesteps: 200 | |
| loss_type: 'l2' | |
| beta_schedule: 'cosine' | |
| n_heads: 8 | |
| mlp_hidden_times: 4 | |
| attn_pd: 0.0 | |
| resid_pd: 0.0 | |
| kernel_size: 1 | |
| padding_size: 0 | |
| solver: | |
| base_lr: 1.0e-5 | |
| max_epochs: 1385 | |
| results_folder: ../../../data/Checkpoints_revenue-1 | |
| gradient_accumulate_every: 1 | |
| save_cycle: 277 # max_epochs // 5 | |
| ema: | |
| decay: 0.995 | |
| update_interval: 10 | |
| scheduler: | |
| target: engine.lr_sch.ReduceLROnPlateauWithWarmup | |
| params: | |
| factor: 0.5 | |
| patience: 200 | |
| min_lr: 1.0e-5 | |
| threshold: 1.0e-1 | |
| threshold_mode: rel | |
| warmup_lr: 8.0e-4 | |
| warmup: 300 | |
| verbose: False | |
| dataloader: | |
| train_dataset: | |
| target: utils.data_utils.real_datasets.RevenueDataset | |
| params: | |
| name: revenue | |
| proportion: 0.8 # Set to rate < 1 if training conditional generation | |
| # data_root: ./Data/datasets/stock_data.csv | |
| data_root: ../../../data/daily.csv | |
| window: 240 # seq_length | |
| save2npy: True | |
| neg_one_to_one: True | |
| seed: 2024 | |
| period: train | |
| test_dataset: | |
| target: utils.data_utils.real_datasets.RevenueDataset | |
| params: | |
| name: revenue | |
| proportion: 0.8 # rate | |
| data_root: ../../../data/daily.csv | |
| window: 240 # seq_length | |
| save2npy: True | |
| neg_one_to_one: True | |
| seed: 2024 | |
| period: test | |
| style: separate | |
| distribution: geometric | |
| coefficient: 1.0e-2 | |
| step_size: 5.0e-2 | |
| sampling_steps: 200 | |
| batch_size: 64 | |
| sample_size: 256 | |
| shuffle: True |